2020-04-29 11:32:49 +08:00
|
|
|
// RUN: npcomp-opt -split-input-file %s | npcomp-opt | FileCheck --dump-input=fail %s
|
2020-04-30 09:20:42 +08:00
|
|
|
|
2020-04-29 11:32:49 +08:00
|
|
|
// -----
|
|
|
|
|
// CHECK-LABEL: @builtin_ufunc
|
2020-06-30 07:19:03 +08:00
|
|
|
func @builtin_ufunc(%arg0 : tensor<3xf64>, %arg1 : tensor<3xf64>) -> tensor<3xf64> {
|
|
|
|
|
%0 = numpy.builtin_ufunc_call<"numpy.add"> (%arg0, %arg1) : (tensor<3xf64>, tensor<3xf64>) -> tensor<3xf64>
|
|
|
|
|
return %0 : tensor<3xf64>
|
2020-04-30 08:49:56 +08:00
|
|
|
}
|
Add support for "trailing_" and "out" variants of various ops.
We already had the `promoteTrailingOutTensor` flag, but weren't using
it. A inplaceVariantKernelName flag needed to be added.
This change is a little dissatisfying, as the conversions done by the
RecognizeKernelsPass are currently non-orthogonal. In particular,
`kDropResultAndAliasArg0` probably won't work as intended if mixed with
these (we probably need to promote kDropResultAndAliasArg0 to not be an
arg-level thing anyway, as we have done with promoteTrailingOutTensor).
This involved adding a new op `numpy.overwrite_array`.
```
numpy.overwrite_array %arg2 overwrites %arg0 : tensor<2x3xf32>, !numpy.ndarray<[2,3]:f32>
```
This models the destructive update behavior. Note that in the above op,
we cannot simply RAUW %arg0 with a suitably conveted %arg2 (for example,
%arg0 might have uses that are not dominated by %arg2, or might have an
alias relation with some other array in the program). In general, we
need a pass analogous to "SSA-formation" which knows how to see through
these to uncover an underlying tensor program.
Also, add tanh_out_e2e.py/div_inplace_e2e.py and fix some bitrot in
refjit.py which is my running example I'm trying to get working.
2021-03-19 04:13:40 +08:00
|
|
|
|
|
|
|
|
// CHECK-LABEL: @ndarray_tensor_bridging
|
|
|
|
|
func @ndarray_tensor_bridging(%arg0: !numpy.ndarray<[2,3]:f32>, %arg1: !numpy.ndarray<[2,3]:f32>, %arg2: tensor<2x3xf32>) {
|
|
|
|
|
// CHECK-NEXT: numpy.copy_to_tensor
|
|
|
|
|
%t = numpy.copy_to_tensor %arg1 : (!numpy.ndarray<[2,3]:f32>) -> tensor<2x3xf32>
|
|
|
|
|
// CHECK-NEXT: numpy.create_array_from_tensor
|
|
|
|
|
%a = numpy.create_array_from_tensor %arg2 : (tensor<2x3xf32>) -> !numpy.ndarray<[2,3]:f32>
|
|
|
|
|
// CHECK-NEXT: numpy.overwrite_array
|
|
|
|
|
numpy.overwrite_array %arg2 overwrites %arg0 : tensor<2x3xf32>, !numpy.ndarray<[2,3]:f32>
|
|
|
|
|
return
|
|
|
|
|
}
|
